This group has already demonstrated the value of explainable AI for a range of medical applications beyond imaging. These include tools for assessing patient risk factors for complications during surgery and targeting cancer therapies based on an individual's molecular profile.
This paper is one of two studies from this group to appear in the current issue of Nature Machine Intelligence. Lee is also the senior and corresponding author on the second paper, "Improving performance of deep learning models with axiomatic attribution priors and expected gradients," for which she teamed up with Janizek, his fellow M.D.-Ph.D. student Gabriel Erion, Ph.D. student Pascal Sturmfels, and affiliate professor Scott Lundberg of Microsoft Research.
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This research was funded by the National Science Foundation and the National Institutes of Health.
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